This monograph is multivariate, multi-perspective and multipurpose. We intend to be innovatively integrative through statistical synthesis. Innovation requires capacity to operate in ways that are not ordinary, which means that conventional computations and generic graphics will not meet the needs of an adaptive approach. Flexible formulation and special schematics are essential elements that must be manageable and economical.

This unique volume presents the scientific achievements, significant discoveries and pioneering contributions of various academicians, industrialist and research scholars. The book is an essential source of reference and provides a comprehensive overview of the author's work in the field of mathematics, statistics and computer science.

“Multi-indicator Systems and Modelling in Partial Order” contains the newest theoretical concepts as well as new applications or even applications, where standard multivariate statistics fail. Some of the presentations have their counterpart in the book; however, there are many contributions, which are completely new in the field of applied partial order.

Statistical Geoinformatics for Human Environment Interface presents two paradigms for studying both space and interface with regard to human/environment: localization and multiple indicators. The first approach localizes thematic targets by treating space as a pattern of vicinities, with the pattern being a square grid and the placement of vicinities centrically referenced. The second approach explores human/environment interface as an abstraction through indicators, neutralizing the common conundrum of how to reconcile disparate spatial structures such as points, lines, and polygons. These paired paradigms enable: The capacity to cope with complexity Systematic surveillance Visualization and communication Preliminary prioritization Coupling of GIS and statistical software Avenues for automation Illustrating the interdisciplinary nature of geoinformatics, this book offers a novel approach to the spatial analysis of human influences and environmental resources. It includes practical strategies for statistical and spatial analysis.

In biological research, the amount of data available to researchers has increased so much over recent years, it is becoming increasingly difficult to understand the current state of the art without some experience and understanding of data analytics and bioinformatics. An Introduction to Bioinformatics with R: A Practical Guide for Biologists leads the reader through the basics of computational analysis of data encountered in modern biological research. With no previous experience with statistics or programming required, readers will develop the ability to plan suitable analyses of biological datasets, and to use the R programming environment to perform these analyses. This is achieved through a series of case studies using R to answer research questions using molecular biology datasets. Broadly applicable statistical methods are explained, including linear and rank-based correlation, distance metrics and hierarchical clustering, hypothesis testing using linear regression, proportional hazards regression for survival data, and principal component analysis. These methods are then applied as appropriate throughout the case studies, illustrating how they can be used to answer research questions. Key Features: · Provides a practical course in computational data analysis suitable for students or researchers with no previous exposure to computer programming. · Describes in detail the theoretical basis for statistical analysis techniques used throughout the textbook, from basic principles · Presents walk-throughs of data analysis tasks using R and example datasets. All R commands are presented and explained in order to enable the reader to carry out these tasks themselves. · Uses outputs from a large range of molecular biology platforms including DNA methylation and genotyping microarrays; RNA-seq, genome sequencing, ChIP-seq and bisulphite sequencing; and high-throughput phenotypic screens. · Gives worked-out examples geared towards problems encountered in cancer research, which can also be applied across many areas of molecular biology and medical research. This book has been developed over years of training biological scientists and clinicians to analyse the large datasets available in their cancer research projects. It is appropriate for use as a textbook or as a practical book for biological scientists looking to gain bioinformatics skills.

Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R, Second Edition focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) a

Trait-based ecology is rapidly expanding. This comprehensive and accessible guide covers the main concepts and tools in functional ecology.

Smallholder farming systems contribute a substantial quantity of the food consumed in many lower and middle-income countries and contribute to the national and local economies. Despite the importance of smallholder farming, a transformation is needed in order to deliver food security and decent incomes for the farmers themselves and at the national level. This transformation must also be sustainable in terms of environmental impacts and social equity in order to be successful in the long term. The pressures of population growth, climate change, and land fragmentation compound the problem. Addressing these overlapping issues is a big challenge. One obstacle is the lack of good quality granular data linking these issues together. Household surveys are the workhorse method for gathering such data, but there are well-known problems that prevent household survey data from building up a “big picture” and delivering insights beyond the geographical boundary of each individual study. Such obstacles include the lack of access to datasets, differences in survey design, and respondent biases. Agile, data-oriented research tools can help to overcome these challenges. We use the term “agile” to imply methods that do not attempt exhaustive measurements, which are designed to be easy to use, and which entail some degree of flexibility in terms of adaptation to local conditions and integration with other tools or methods. Often these methods also nudge the behavior of tool users towards best practices. In recent years various research tools and approaches have been published which fit within our definition of “agile data-oriented research tools”. The domains these tools function in include monitoring and evaluation, intervention targeting, tailored information delivery, citizen science, credit scoring, and user feedback collection; all with the over-arching aim to improve data quality and access for those studying the sustainable development of smallholder farming systems. The goal of this Research Topic is to better define that niche, the ecosystem of tools and current practices, and to explore how such approaches can provide the underpinning knowledge required for the transformation of smallholder farming systems. One example of an agile data-oriented research tool is the Rural Household Multi-Indicator Survey (RHoMIS). It is a modular, digital system for building household surveys addressing the common topics in smallholder development. It was purposefully designed to give a broad overview of the farm system whist keeping survey duration to a minimum, to be user-friendly in implementation, and to be sufficiently flexible to function in a broad variety of locations and projects. Since 2015 it has been used by 30 organizations in 32 countries to interview over 34,000 households. The tool and database are open access and a community of practice is developing around the tool. We particularly welcome contributions that engage with the RHoMIS tool and data. However, we also describe the tool in order to provide an example of what is meant by an agile data-oriented research tool, and welcome contributions focusing on other tools or methodologies. We encourage the submission of manuscripts addressing the above topic, and those which fit within one of the following three sub-themes: (i) Perspectives or review articles which explore the niche, best practices, or promising approaches in agile data-oriented research tools for smallholder farm system transformation. Also, technology and code articles that describe new tools are welcomed. (ii) Original research articles presenting analyses based on data derived from agile data-oriented tools used at the project level. Examples include impact evaluations, adoption studies, targeting studies, or adaptive management, and should reflect on the additional benefit leveraged by the agile method applied. (iii) Original research articles that make use of the large amounts of data generated by such agile methods and/or link between agile data and other data sources. Examples include meta-analyses of data from multiple studies, layering data collected from different agile tools, or linking agile data to remote sensing or large-scale modeling outputs.